How to Bootstrap DEA Estimators: A Monte Carlo Comparison

Mickael Löthgren

32 citations0/0 files storedRePEc: Research Papers in Economics

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Efficiency Analysis Using DEA Sports Analytics and Performance Advanced Statistical Methods and Models

Abstract

This paper evaluates the performance of three bootstrap algorithms for the data envelopment analysis (DEA) estimator using a Monte Carlo simulation study. The Löthgren and Tambour (1997) (LT) algorithm; the Simar and Wilson (1997b) (SW) algorithm; and a combination of the LT and SW algorithms (the LSW-algorithm) are considered in the study. The empirical coverage accuracy of bootstrap confidence intervals are simulated under both variable returns to scale (VRS) and constant return-to-scale (CRS) restricted DEA estimators. The results indicate that the LSW-algorithm performs slightly better than the LT-algorithm, which in turn performs better than the SW-algorithm.

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